fashionmnist数据集_pytorch进行fashion mnist数据集分类
时间: 2023-08-30 07:09:48 浏览: 77
好的,以下是使用PyTorch对Fashion MNIST数据集进行分类的示例代码:
首先,我们需要导入必要的库和模块:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
```
然后,我们需要下载并加载数据集。Fashion MNIST数据集可以通过以下方式下载:
```python
train_data = datasets.FashionMNIST(
root="data",
train=True,
download=True,
transform=transforms.ToTensor()
)
test_data = datasets.FashionMNIST(
root="data",
train=False,
download=True,
transform=transforms.ToTensor()
)
```
接下来,我们需要定义一个神经网络模型。在这个例子中,我们使用了一个简单的卷积神经网络:
```python
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.layer1 = nn.Sequential(
nn.Conv2d(1, 32, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.layer2 = nn.Sequential(
nn.Conv2d(32, 64, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.fc = nn.Sequential(
nn.Linear(7 * 7 * 64, 128),
nn.ReLU(),
nn.Linear(128, 10)
)
def forward(self, x):
out = self.layer1(x)
out = self.layer2(out)
out = out.reshape(out.size(0), -1)
out = self.fc(out)
return out
```
然后,我们需要定义损失函数和优化器:
```python
model = CNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
```
最后,我们可以开始训练模型并评估其性能:
```python
train_loader = DataLoader(train_data, batch_size=100, shuffle=True)
test_loader = DataLoader(test_data, batch_size=100, shuffle=False)
for epoch in range(10):
for i, (images, labels) in enumerate(train_loader):
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
if (i + 1) % 100 == 0:
print(f"Epoch [{epoch + 1}/{10}], Step [{i + 1}/{len(train_loader)}], Loss: {loss.item():.4f}")
with torch.no_grad():
correct = 0
total = 0
for images, labels in test_loader:
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
accuracy = 100 * correct / total
print(f"Test Accuracy: {accuracy:.2f}%")
```
这就是使用PyTorch对Fashion MNIST数据集进行分类的示例代码。希望能对你有所帮助!